A classifier may be trained using a machine learning algorithm to classify a plurality of elements as one of a plurality of classes. The classifier makes a prediction of the classification of each element, and may also make an assessment of the confidence in the prediction.
One existing example of a classifier is a boosted classifier, which combines the output of a set of “weak” classifiers (with low, but better than random, classification accuracy) to produce a single “strong” classifier with high accuracy. In combining the weak classifiers, the result of each weak classifier is weighted according to the confidence in the correctness of the weak classifier. A boosting algorithm may adaptively improve the performance of the classifier by giving greater weight to examples misclassified by a weak classifier when training the next weak classifier.
Despite combining a number of weak classifiers, the strong classifier will not necessarily produce an accurate classification for a given problem. For example, if the boosted classifier is used to segment or select features in an image, some of the pixels in the image may be mislabelled, leading to noise in the resulting segmentation.
This noise may be cleaned up to an extent by using simple morphological filtering operations such as erosion and dilation of the labels to remove small regions of noise. Erosion decreases the size of objects and removes anomalies, dilation increases the size of objects and fills in holes. However, the standard formulation of these operations renders them simplistic in nature and hence they are unable to incorporate additional information when applied.
Other existing techniques include using a more sophisticated dilation technique to refine results. On each dilation step, the likelihood of points near the boundary of a segmented region belonging to that region is computed, and points with likelihoods above the threshold are added. The likelihood is based on image intensity within the region.
Another alternative is to use a colour watershed segmentation to improve classification results. The classification is used to extract seed points for each region. Watershed segmentation is then applied to expand these regions until all pixels have been labelled. This expansion is based on region colour information.
It is an aim of the present invention to provide an alternative method for improving classification results of a classifier.
The above discussion of background art is included to explain the context of the present invention. It is not to be taken as an admission that any of the documents or other material referred to was published, known or part of the common general knowledge at the priority date of any one of the claims of this specification.